Learning Insulin-Glucose Dynamics in the Wild
AuthorsAndrew C. Miller, Nicholas J. Foti, Emily Fox
We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics driven by a machine learning sequence model. Our model maintains a physiologically plausible inductive bias and clinically interpretable parameters — e.g., insulin sensitivity — while inheriting the flexibility of modern pattern recognition algorithms. Critical to modeling success are the flexible, but structured representations of subject variability with a sequence model. In contrast, less constrained models like the LSTM fail to provide reliable or physiologically plausible forecasts. We conduct an extensive empirical study. We show that allowing biomedical model dynamics to vary in time improves forecasting at long time horizons, up to six hours, and produces forecasts consistent with the physiological effects of insulin and carbohydrates.
As the COVID-19 pandemic took off during early 2020, widespread interest in modeling the trajectory of infections emerged. This interest was predicated on the hope that accurate models could be developed and subsequently used to help governments and policy makers monitor the effect of lockdowns and determine safe points in time to reopen.